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Title	Intelligent Monitoring And Management Of Smart Buildings Using Machine Learning: Optimizing User Behavior And Energy Efficiency
ID_Doc	32444
Authors	Nijim M.; Kanumuri V.; Albetaineh H.; Goyal A.
Year	2023
Published	Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
DOI	http://dx.doi.org/10.1109/CSCE60160.2023.00387
Abstract	Smart buildings are a crucial component of smart cities and smart grids, contributing to a more intelligent urban environment. The efficient utilization of energy in buildings is now a significant concern for sustainable societies. Whether they are residential or industrial. Smart buildings consume the majority of the energy produced, but in the context of smart grids, they are also designed to generate energy and help stabilize demand. Even small fluctuations in peak demand can result in substantial budget saving for customers and utilities. In this research, we explore the applicability of machine learning (ML) methods for load forecasting in smart buildings using smart sensor data that infers user behavior. Here, we utilized a smart building dataset encompassing four floors, 51 rooms, and 255 integrated sensors. Each smart building room comprises five types of calculations, including a P IR Sensor that helps to capture motion, a Carbon dioxide concentration sensor to check the purity of air, a Sensor to calculate temperature, a humidity sensor to maintain good conditioning, and a luminosity sensor. By using this data from these sensors, we can optimize and manage the energy saving. To evaluate the prediction performance of the input variables, we used to compare them we use mean squares error, mean absolute error and root mean square error. © 2023 IEEE.
Author Keywords	carbon dioxide sensor; Humidity sensor; luminosity sensor; machine learning; PIR sensor

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